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The design of multi-touch multiuser tabletop user interfaces is still in its infancy and is not yet well understood. To date, published experimental results have primarily focused on controlled user studies. In this paper, we present observations of user experience "in the wild" on interactive tables in four different real-world contexts-all non-controlled settings. We reflect upon our collective experience, report our observations, and summarize lessons learned by identifying design considerations relating to several aspects of interactive tables, such as simultaneous touching, ambiguous input, one-fingered touch, finger resolution, alternate touch input, crowding and clutter, text input, orientation, multiuser coordination, occlusion, ergonomic issues, and mental models.

Construction toys are a superb medium for creating geometric models. We argue that such toys, suitably instrumented or sensed, could be the inspiration for a new generation of easy-to-use, tangible modeling systems-especially if the tangible modeling is combined with graphical-interpretation techniques for enhancing nascent models automatically. The three key technologies needed to realize this idea are embedded computation, vision-based acquisition, and graphical interpretation. We sample these technologies in the context of two novel modeling systems: physical building blocks that self-describe, interpret, and decorate the structures into which they are assembled; and a system for scanning, interpreting, and animating clay figures.

Tables provide a large and natural interface for supporting direct manipulation of visual content for human-to-human interactions. Such surfaces also support collaboration, coordination, and parallel problem solving. However, the direct-touch table metaphor also presents considerable challenges, including the need for input methods that transcend traditional mouse-and keyboardbased designs.

Scheduling, routing, and layout tasks are examples of hard operations-research problems that have broad application in industry. Typical algorithms for these problems combine some form of gradient descent to find local minima with some strategy for escaping nonoptimal local minima. Our idea is to divide these two subtasks cleanly between human and computer: in our paradigm of human-guided simple search the computer is responsible only for finding local minima using a simple hill-climbing search; using visualization and interaction techniques, the human user identifies promising regions of the search space for the computer to explore, and intervenes to help it escape nonoptimal local minima. We have applied our approach to the problem of capacitated vehicle routing with time windows, a commercially important problem with a rich research history. Despite its simplicity, our prototype system is competitive with the majority of previously reported systems on benchmark academic problems, and has the advantage of keeping a human tightly in the loop to handle the complexities of real-world applications.

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